E0 270: Machine Learning, February Term 2021

E0 270: Machine Learning
(Feb-May 2021)

Department of Computer Science & Automation
Indian Institute of Science

[Course Description] [Projects] [Academic Honesty] [Syllabus]

Course Information

Class Meetings

Lectures: Tuesday, Thursday 11:30am-01:00pm
MS Teams (online)
First lecture: Thu, Feb 25th

Tutorial/discussion sessions will be scheduled on an on-going basis.

Instructor

Prof. Ambedkar Dukkipati (ambedkar@iisc)

TAs

Nabanita Paul [nabanitapaul@iisc.ac.in]
Tony Gracious [tonygracious@iisc.ac.in]
Shaarad A R [rangaa@iisc.ac.in]
Mariamma Antony [mariammaa@iisc.ac.in]
Shubham Gupta [shubhamg@iisc.ac.in]
Chaitanya Murti [mchaitanya@iisc.ac.in]

Web Support

Aakash Patel [aakashpatel@iisc.ac.in]

Course Evaluation (Final)

Mid Term: 20 Marks
Assignments: 30 Marks
Participation in the Discussion and Doubt Solving: 10 Marks
Project mid evaluation: 10 Marks
Project Final evaluation: 30 Marks

Announcements

Check Teams time to time.

Course Description

With the increasing amounts of data being generated in diverse fields such as astronomical sciences, health and life sciences, financial and economic modeling, climate modeling, market analysis, and even defense, there is an increasing need for computational methods that can automatically analyze and learn predictive models from such data. Machine learning, the study of computer systems and algorithms that automatically improve performance by learning from data, provides such methods; indeed, machine learning techniques are already being used with success in a variety of domains, for example in computer vision to develop face recognition systems, in information retrieval to improve search results, in computational biology to discover new genes, and in drug discovery to prioritize chemical structures for screening. This course aims to provide a sound introduction to both the theory and practice of machine learning, with the goal of giving students a strong foundation in the subject, enabling them to apply machine learning techniques to real problems, and preparing them for advanced coursework/research in machine learning and related fields.

Preferred background

E0 232: Probability and Statistics (or equivalent course elsewhere) and earned a grade of B or higher. In addition, some background in linear algebra and optimization will be helpful.

Academic Honesty

As students of IISc, we expect you to adhere to the highest standards of academic honesty and integrity.

Elements of the course are designed to support your learning of the subject. Copying will not help you (in the exams or in the real world), so don't do it. If you have difficulties learning some of the topics or lack some background, try to form study groups where you can bounce off ideas with one another and try to teach each other what you understand. You're also welcome to talk to any of us and we'll be glad to help you.

If any exam/report is found to be copied, it will automatically result in a zero grade for that exam/project and a warning note to your advisor. Any repeat instance will automatically lead to a failing grade in the course.

Course Material (Topic wise)

	Topics	Lecture Notes	Remarks
1	Introduction, What is Data and Model, Machine Learning Workflow, Distance Based Classifiers, Bayes Decision Theory	slides1
2	Different types of Learning, Supervised Learning, Foundational Aspects of ML, Linear Regression	slides2
3	Probabilistic view of Linear Regression, Logistic Regression, Hyperplane based Classifiers and Perceptron	slides3
4	Support Vector Machines, Kernel Methods	slides4
5	Feed Forward Neural Networks, Backpropagation algorithm, CNNs, RNNs	slides5
6	Unsupervised Learning, Dimentionality Reduction, K-Means Clustering	slides6
7	Spectral Clustering	slides7
8	Probabilistic Models, Graphical Models, Markov Random Fields, Markov Chain, Monte Carlo Methods, Restricted Boltzmann Machines	see lecutre video
9	Latent Variable Models, Gaussian Mixture Models, Free Energy Optimization, Expectation Maximization algorithm	see lecutre video
10	Model Selection, Making ML algorithms work	slides8

Course Tutorial

	Topics	Date	TA
1	Linear Regression, Logistic Regression, Naive Bayes, Bayes Decision Theory	March 23, 2021	Shubham Gupta, Nabanita Paul
2	Generative modeling - GANs and VAEs		Chaitanya Murti
3	Autoencoder and RNNs	May 1, 2021	Tony Gracious

References

Recommended textbooks:

C.M. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
Hastie T, Tibshirani R and Friedman J, The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer, 2nd Edition, 2009
Haykin. S, Neural Networks and Learning Systems, Prentice-Hall, 3rd Edition, 2009
Goodfellow, Bengio, Courville, Deep Learning, MIT Press, 2017

Additional textbooks:

T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2nd Edition, 2009. [free download available]
T. Mitchell, Machine Learning. McGraw Hill, 1997.

(Optimization texbooks)

Projects

As part of the this course, you are required to work on a project. This will give you hands-on experience of working with data from various domains - text, images, videos etc that are used in the contemporary ML research, and also expose you to some specialized topics of ML that are more advanced or recent than what will be covered in the lectures.

A list of project ideas will be mailed on the mailing list. You can also come up with your own projects ideas.

Project Policy

Some projects ideas are based on reading and survey of several papers, and others are based on implementation of one or two papers, and comparison of the methods proposed in them over datasets. We will point you to relevant papers and datasets. You can read/implement other relevant papers and datasets if you want. You can also innovate and come up with your own approaches for solving the problems.
The best grades will be reserved for the groups which come up with some new approaches, either theoretically or experimentally, which have not been used in the literature.
Each project will have an associated project mentor. He/she will help you to locate papers and datasets, and clear doubts.
In implementation-based projects, it may so happen that codes are already available online. It is perfectly fine to use them. Only, that should be cited in the reports.
You are required to provide a mid-term report around the third week of March, and the final report shortly before the final examination.
Plagiarism is strictly prohibited while writing the reports. Any plagiarism detected will result in F grade for the course.

Course Project Presentation

Will be updated later

E0 270: Machine Learning (Feb-May 2021)

Department of Computer Science & AutomationIndian Institute of Science